Guo Chao, Wei Bangguo, Lan Bin, Liang Lunfei, Liu Houde
Jianghuai Advanced Technology Center, Hefei 230031, China.
Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel). 2024 Oct 11;24(20):6559. doi: 10.3390/s24206559.
A stable and robust odometry system is essential for autonomous robot navigation. The 4D millimeter-wave radar, known for its resilience in harsh weather conditions, has attracted considerable attention. As the latest generation of FMCW radar, 4D millimeter-wave radar provides point clouds with both position and Doppler velocity information. However, the increased uncertainty and noise in 4D radar point clouds pose challenges that prevent the direct application of LiDAR-based SLAM algorithms. To address this, we propose a SLAM framework that fuses 4D radar data with gyroscope readings using graph optimization techniques. Initially, Doppler velocity is employed to estimate the radar's ego velocity, with dynamic points being removed accordingly. Building on this, we introduce a pre-integration factor that combines ego-velocity and gyroscope data. Additionally, leveraging the stable RCS characteristics of radar, we design a corresponding point selection method based on normal direction and propose a scan-to-submap point cloud registration technique weighted by RCS intensity. Finally, we validate the reliability and localization accuracy of our framework using both our own dataset and the NTU dataset. Experimental results show that the proposed DGRO system outperforms traditional 4D radar odometry methods, especially in environments with slow speeds and fewer dynamic objects.
一个稳定且强大的里程计系统对于自主机器人导航至关重要。以在恶劣天气条件下的适应性而闻名的4D毫米波雷达，已引起了相当大的关注。作为最新一代的FMCW雷达，4D毫米波雷达提供具有位置和多普勒速度信息的点云。然而，4D雷达点云中增加的不确定性和噪声带来了挑战，阻碍了基于激光雷达的SLAM算法的直接应用。为了解决这个问题，我们提出了一个使用图优化技术将4D雷达数据与陀螺仪读数融合的SLAM框架。最初，利用多普勒速度来估计雷达的自身速度，并相应地去除动态点。在此基础上，我们引入了一个结合自身速度和陀螺仪数据的预积分因子。此外，利用雷达稳定的RCS特性，我们设计了一种基于法线方向的相应点选择方法，并提出了一种由RCS强度加权的扫描到子地图点云配准技术。最后，我们使用自己的数据集和NTU数据集验证了我们框架的可靠性和定位精度。实验结果表明，所提出的DGRO系统优于传统的4D雷达里程计方法，特别是在速度较慢且动态物体较少的环境中。
